基于X-12-ARIMA季节分解与年度电量校正的月度电量预测

doi:10.3969/j.issn.1000－7229.2017.01.010

摘要/Abstract

摘要： 月度电量预测是电力计划部门安排运行计划与制定购售电计划的基础。提出一种综合考虑多种经济因素的月度电量预测方法。首先，采用X-12-ARIMA模型对月度电量和多种经济因素进行季节分解，并利用逐步回归分析研究各经济量与用电量的关联关系和回归模型，获得初步预测结果；然后，利用多项式拟合进行年度电量预测，并对已有月度电量预测结果进行调整；最后，采用自回归积分滑动平均模型(autoregressive integrated moving average model, ARIMA)对受气象与节假日因素影响较大的月份进行分季节预测修正，获得精度良好的月度电量预测模型。该文采用广东省2009年3月至2014年4月的经济数据与电量数据对2014年5月至2015年4月的电量数据进行预测。预测结果的平均预测精度为97.78 %，验证了预测模型的有效性。

关键词: X-12-ARIMA, 月度电量, 预测, 校正, 自回归积分滑动平均模型(ARIMA)

Abstract: Monthly electricity forecast is the basis for the planning department of power grid corporation to arrange the operation and purchase plan. This paper presents a method for forecasting monthly electricity consumption considering various economic factors. First, we adopt the X-12-ARIMA model to decompose the economic data and monthly electricity data into seasonal part, and adopt stepwise regression analysis to study the correlation and regression model between the economic factors and electricity consumption and get the primary forecast results. Then, we use polynomial fitting to forecast the annual electricity, and use the result of annual electricity to adjust the monthly electricity consumption. Finally, we adopt autoregressive integrated moving average (ARIMA) model to correct the seasonal forecast for the months which are obviously influenced by meteorological factors and holidays, and obtain the monthly electricity forecasting model with good precision. In this paper, monthly electricity consumption from May 2014 to April 2015 in Guangdong Province are forecasted by economic data and monthly electricity consumption from March 2009 to April 2014 to obtain the average prediction accuracy of 97.78%, which verifies the effectiveness of the prediction model.

Key words: X-12-ARIMA, monthly electricity consumption, forecasting, correction, autoregressive integrated moving average (ARIMA)

中图分类号:

TM 714

张强，王毅，李鼎睿，朱文俊. 基于X-12-ARIMA季节分解与年度电量校正的月度电量预测[J]. 电力建设, 2017, 38(1): 76-.

ZHANG Qiang, WANG Yi, LI Dingrui, ZHU Wenjun. Monthly Electricity Forecast Based on X-12-ARIMA Seasonal Decomposition and Annual Electricity Correction[J]. Electric Power Construction, 2017, 38(1): 76-.

参考文献

［1］康重庆,夏清,张伯明. 电力系统负荷预测研究综述与发展方向的探讨［J］. 电力系统自动化，2004,28(17):1-11.

KANG Chongqing, XIA Qing, ZHANG Boming. Review of power system load forecasting and its development ［J］.Automation of Electric Power Systems， 2004,28(17):1-11.

［2］边瑞恩. 广东省月度负荷预测建模研究［D］.广州：华南理工大学,2013.

BIAN Rien.Study on monthly load forecasting model of guangdong province［D］. Guangzhou： South China University of Technology, 2013.

［3］郭鸿业,陈启鑫,夏清,等. 考虑经济因素时滞效应的月度负荷预测方法［J］. 电网技术,2016,40(2):514-520.

GUO Hongye, CHEN Qixin, XIA Qing,et al. Study on mid-term electricity load forecast considering time lag effects of economic factors［J］. Power System Technology,2016,40(2):514-520.

［4］温丽丽,刘俊勇,吴志云,等. 基于月度滚动修正的合同电量分解算法与应用［J］. 现代电力,2008,25(1):82-87.

WEN Lili,LIU Junyong, WU Zhiyun, et al. Study and application of resolution algorithm of contract volume based on monthly rolling amendment［J］. Modern Electric Power,2008,25(1):82-87.

［5］孙小江,王帅,李伟. 月度电量滚动模型与算法研究［J］. 电工技术,2011(10):27-29.

SUN Xiaojiang, WANG Shuai, LI Wei. Study on monthly electricity rolling model and algorithm［J］. Electric Engineering,2011(10):27-29.

［6］颜伟,程超,薛斌,等. 结合X12乘法模型和ARIMA模型的月售电量预测方法［J］. 电力系统及其自动化学报, 2016 , 28(5):76-80.

YAN Wei，CHENG Chao，XUE Bin, et al. Forecasting for monthly electricity consumption using X12 multiplication method and ARIMA model［J］. Proceedings of the CSU-EPSA,2016, 28(5):76-80.

［7］樊娇,冯昊,牛东晓,等. 基于小波分析和GM-ARIMA模型的月度售电量预测［J］. 华北电力大学学报(自然科学版),2015,42(4):101-105.

FAN Jiao, FENG Hao, NIU Xiaodong,et al. Monthly electricity sales forecast based on wavelet analysis and GM-ARIMA model［J］.Journal of North China Electric Power University(Natural Science Edition),2015,42(4):101-105.

［8］贺凤羊，刘建平.如何对中国CPI进行季节调整［J］. 数量经济技术经济研究,2011(5):110-124.

HE Fengyang, LIU Jianping. How to do seasonal adjustment of China’s CPI［J］. The Journal of Quantitative & Technical Economics,2011(5):110-124.

［9］United States Census Bureau. X-13ARIMA-SEATS seasonal adjustment program ［EB/OL］.［2016-03-01］.https://www.census.gov/srd/www/x13as

［10］郑晓雨,贺仁睦,马进. 逐步多元回归法在负荷模型扩展中的应用［J］. 中国电机工程学报, 2011, 31(4) : 72-77.

ZHENG Xiaoyu, HE Renmu, MA Jin. Application of stepwise multiple regression to load model extension ［J］. Proceedings of the CSEE, 2011, 31(4) : 72-77.

［11］季泽宇，袁越，邹文仲. 改进偏最小二乘回归在电力负荷预测中的应用［J］. 电力需求侧管理, 2011, 13(1) : 10-14.

JI Zeyu,YUAN Yue,ZOU Wenzhong. Application of improved partial least square regressive model in power load forecasting［J］. Power Demand Side Management, 2011, 13(1) : 10-14.

［12］毛李帆,江岳春,龙瑞华,等. 基于偏最小二乘回归分析的中长期电力负荷预测［J］. 电网技术,2008,32(19):71-11.

MAO Lifan,JIANG Yuechun,LONG Ruihua,et al. Medium and long-term load forecasting based on partial least squares regression analysis［J］. Power System Techology, 2008,32(19):71-11.

［13］杨建萍.基于ARIMA模型的用电量时间序列建模和预报［J］. 工程数学学报, 2008, 25(4) : 611-615.

YANG Jianping. ARIMA time series modeling and forecasting of electricity consumption［J］.Chinese Journal of Engineering Mathematics, 2008, 25(4) : 611-615.

［14］麦鸿坤，肖坚红，吴熙辰，等. 基于R语言的负荷预测ARIMA模型并行化研究［J］. 电网技术, 2015, 39(11) : 3217-3220.

MAI Hongkun, XIAO Jianhong, WU Xichen，et al. Research on ARIMA model parallelization in load prediction based on R language［J］.Power System Techology , 2015, 39(11) : 3217-3220.

［15］ROB H. Forecast: forecasting functions for time series and linear models［EB/OL］.［2016-07-02］. http://github.com/robjhyndman/forecast

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